Climate Reference Network Metadata

Climate Reference Network Metadata

This is anquick look at the summary statistics of the metadata collected for the Climate Reference Network (CRN). Additional details and finalizing charts will be ongoing. In addition, the same code will be applied to the GHCN V4 Stations. Filenames are currently not portable, but once the git repository is complete, this document should be locally excutable provided you have all the right libraries installed,

Station Locations

For this excercise 114 CRN stations located in CONUS were used to create a metadata set. The metadata collected includes: urban area, population, land cover, terrain, distance to airports, and distance from populated places

usa <- get_map(location=c(min(CRN$Longitude)-5,min(CRN$Latitude)-5,
                             max(CRN$Longitude)+5,max(CRN$Latitude)+5),
                             source="stamen",maptype="watercolor")
f1 <-  ggmap(usa)+ 
       geom_point(data=CRN, aes(x=CRN$Longitude,y=CRN$Latitude),alpha= .5, size=2, color ="blue") + 
       xlab("Longitude")+ylab("Latitude") + ggtitle("CRN Locations",subtitle="Conus")
f1

Local Area Covered by Water

For every site we calculate the percentage of water within a 10km radius using the ESA 300meter Landcover product

f2 <- ggplot(CRN, aes(100* WaterArea/314))+geom_histogram() +
      ggtitle("Percentage Water Cover",subtitle = "10 km radius")+
      xlab("Percentage") + ylab("Station Count")
f2

Local Urban Area

For every site we calculate the percentage of urban landcover within a 10km radius using the ESA 300meter Landcover product

f3 <- ggplot(CRN, aes(100* UrbanArea10K/314))+geom_histogram() +
      ggtitle("Percentage Urban Cover",subtitle = "10 km radius")+
      xlab("Percentage") + ylab("Station Count")
f3

One critical Metric suggested by Lee is the urban scale length which is simply the square root of the urban area.

T.-W. Lee, Heung S. Choi, and Jinoh Lee, “Generalized Scaling of Urban Heat Island Effect and Its Applications for Energy Consumption and Renewable Energy,” Advances in Meteorology, vol. 2014, Article ID 948306, 5 pages, 2014. doi:10.1155/2014/948306

f4 <- ggplot(CRN, aes(  UrbanArea10K^.5))+geom_histogram() +
      ggtitle("Urban Cover Scale Length")+
      xlab("Scale Length") + ylab("Station Count")
f4

To get a visual sense of the amount of urbanity these figures represent we look at a google map visualization and select stations with the highest amount of urban area, roughly 20% urban

 CRN <- CRN %>% arrange(desc(UrbanArea10K))
 m  <-  get_map(location=c(lon=CRN$Longitude[1] ,
                           lat=CRN$Latitude[1]),zoom= 13,maptype="satellite",source="google")            
                
 f5  <- ggmap(m )+
        geom_point(data=CRN,aes(x=Longitude[1],y=Latitude[1]),color="blue",size=2)+
        ggtitle(CRN$Name[1], subtitle = paste("Urban Area: ",round(CRN$UrbanArea10K[1],1)," Sq km ",sep=""))
 f5

 m2  <- get_map(location=c(lon=CRN$Longitude[2] ,lat=CRN$Latitude[2]),zoom= 13,
                maptype="satellite",source="google")
 f6  <- ggmap(m2 ) + geom_point(data=CRN,aes(x=Longitude[2],y=Latitude[2]),color="blue",size=2)+
        ggtitle(CRN$Name[2],subtitle = paste("Urban Area: ",round(CRN$UrbanArea10K[2],1)," Sq km ",sep=""))
                                           
 f6

Elevation

Every site has an Elevation at the exact lite location. In addition the metadata includes the average elevation for the surrounding arc minute ( ~ 2km at the equator). The difference between these can indicate whether a site may be susceptible to cold air drainage.

f7 <- ggplot(CRN, aes(  Elevation))+geom_histogram() +
      ggtitle("Station Elevation")+
      xlab("Elevation (meters) ") + ylab("Station Count")

f7

f8 <- ggplot(CRN, aes(  DEM1km))+geom_histogram() +
      ggtitle("Station Elevation DEM")+
      xlab("Elevation (meters) ") + ylab("Station Count")

f8

f9 <- ggplot(CRN, aes(   Elevation-DEM1km))+geom_histogram() +
      ggtitle("Station Height above local average terrain")+
      xlab("Difference (meters) ") + ylab("Station Count")

f9

Distance to Coast

The distance to the continental coast is calculated for each station. Negative values represent a point inside of the continent, positive values would indicate an island, bouy or ship location offshore

f10 <- ggplot(CRN, aes( DistancetoCoast))+geom_histogram() +
      ggtitle("Station Distance From Coast")+
      xlab("Distance (km) ") + ylab("Station Count")

f10

Landform

In addition to the elevation and the local DEM elevation we also collect the land form of the site which indicates whether the site is on smooth plains, hills, valleys.

f11 <- ggplot(CRN, aes( EF_LF_Desc))+geom_bar() +
      ggtitle("Landform")+
      xlab("Landform Type") + ylab("Station Count")

f11

Landclass

We use the ESA 300 meter land class data ( 2008-2012 epoch) to ascertain the land cover for the sites’ location. The average user accuracy is ~74%, depending on the land class.

f12 <- ggplot(CRN, aes( LCCOwnLabel))+geom_bar() +
      coord_flip()+
      ggtitle("Land Cover at the site")+
      xlab("Landform Type") + ylab("Station Count")

f12

There are two stations that indicate the site is located on an urban pixel. Since the user accurary for urban pixels is on the oer of 85%, that could be due to: misclassification, registration errors, site location errors. A quick check of the two sites seems to indicate a mis classification

 LC <- CRN %>% filter(LCCOwnLabel=="Urban areas")
 m3  <-  get_map(location=c(lon=LC$Longitude[1] ,
                           lat=LC$Latitude[1]),zoom= 19,maptype="satellite",source="google")            
                
 f13  <- ggmap(m3 )+
        geom_point(data=LC,aes(x=Longitude[1],y=Latitude[1]),color="blue",size=2)+
        ggtitle(LC$Name[1], subtitle = paste("Land Class: ",LC$LCCOwnLabel[1],sep=""))
 f13

 m4  <- get_map(location=c(lon=LC$Longitude[2] ,lat=LC$Latitude[2]),zoom= 19,
                maptype="satellite",source="google")
 f14  <- ggmap(m4 ) + geom_point(data=LC,aes(x=Longitude[2],y=Latitude[2]),color="blue",size=2)+
        ggtitle(LC$Name[2], subtitle = paste("Land Class: ",LC$LCCOwnLabel[1],sep=""))
                                           
 f14

Gridded Population

For every station we extract the population count from GPW version 4. The gridded data is a 30 arc second dataset or a 1km resolution at the equator. Since grid cell size changes with latitude other equal area representations (10km)are included as well. For GPW4 we inlcude 4 reference dates, 2000, 2005,2010, and 2015.

POP <- CRN %>% select(Name,GPwV4_00,GPwV4_05,GPwV4_10,GPwV4_15) %>%
       gather(Year,Population,starts_with("GP")) %>% mutate(Year=as.factor(Year))
   
f15<- ggplot(POP,  aes(Population,fill=Year))+geom_histogram()+facet_wrap(~Year,nrow=2,ncol=2) +
      ggtitle("Gridded Population Count")
f15

f16<- ggplot(CRN,  aes(GPwV4_15_10km))+geom_histogram() +
      ggtitle("Gridded Population Count",subtitle="10Km radius") 
       
f16

f17 <-ggplot(CRN,  aes(GPwV4_15-GPwV4_00 ))+
      geom_histogram()+
      ggtitle("Population Growth 2000-2015")+
      xlab("Increase")+ylab("Station Count")
      
f17

Hyde Population

For every station we extract the population count from Hyde version 3.1. The gridded data is a 5 minute dataset or a 8km resolution at the equator. Since grid cell size changes with latitude other equal area For Hyde we inlcude 5 reference dates: 1970, 1980,1990, 2000, and 2005.

HYDE <- CRN %>% select(Name,Hyde1970,Hyde1980,Hyde1990,Hyde2000,Hyde2005) %>%
       gather(Year,Population,starts_with("Hy")) %>% mutate(Year=as.factor(Year))
   
f18<- ggplot(HYDE,  aes(Population,fill=Year))+geom_histogram()+facet_wrap(~Year,nrow=2,ncol=3) +
      theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
      ggtitle("Hyde Gridded Population Count")
f18

f19 <-ggplot(CRN,  aes(Hyde2005-Hyde1970 ))+
      geom_histogram()+
      ggtitle("Population Growth 1970-2005")+
      xlab("Increase")+ylab("Station Count")
      
f19

Population Density

The population density of the site can be calculated by dividing the population count by the area of the cell which is a function of latitute in addition for the 10km population we cans imply divide by the area of a circle with radius 10km. Subtracting these two can give a crude indication of whether a site is more heavily populated at the exact site location or whether the surrounding area has a higher population.

 f20 <-ggplot(CRN,  aes(GPW10km_15_Density ))+
      geom_histogram()+
      ggtitle("Population Density 2015",subtitle="10km radius")+
      xlab("Population Density per sqkm")+ylab("Station Count")
      
f20

f21 <-ggplot(CRN,  aes(GPW10km_15_Density-(GPwV4_15/GPwV4_Area) ))+
      geom_histogram()+
      ggtitle("2015 Population Density Difference",subtitle="10km -Site")+
      xlab("Density Difference")+ylab("Station Count")
      
f21

Isolation from Urban Areas

In addition the site characteristics and immediate vincity, we also calculate the distance from the site to areas of concentrated population. Several measures are provided. The distance to the closest populated place; the distance to the closest place with a population (circ 2000) greater than 50,000; the distance to the closest place with a population (circ 2000) greater than 500,000;the distance to the closest place with a population (circ 2000) greater than 1 Million;the distance to the closest place with a population (circ 2000) greater than 5M. For reference, “Simulations of the London urban heat island” S. I. Bohnenstengel had results that indicated UHI could advect up to 40km under certain conditions, and Basset (“Observations of urban heat island advection from a high-density monitoring network”) indicated advection under certain conditions extending to at least 15km from Birmingham ( Population 1M). Finally, Brandsma (“EMPIRICAL ESTIMATION OF THE EFFECT OF URBAN HEAT ADVECTION ON THE TEMPERATURE SERIES OF DE BILT (THE NETHERLANDS)”), indicates that roughly 10% of the warming seen at De Bilt or .1C out of 1C, is due to advection of UHI from the surrounding cities. Utrecht, the largest city at ~250K, is around 2km from the site and one smaller town (60K) is ~3km from the site.

DIST <- CRN %>% select(Name,DistanceToPlace50K,DistanceToPlace500K,DistanceToPlace1M,DistanceToPlace5M) %>%
  gather(PlaceSize,Distance, starts_with("DistanceToPlace")) %>% 
  mutate(PlaceSize=str_replace(PlaceSize,"DistanceToPlace","")) %>%
  mutate(PlaceSize=as.factor(PlaceSize)) %>%filter(Distance < 125)

f22 <- ggplot(DIST,  aes(Distance,fill=PlaceSize))+geom_histogram(bins=50)+
       facet_wrap(~PlaceSize,nrow=2,ncol=2)+
       ylab("Station Count")+
       ggtitle("Isolation From Urban Centers")
f22

Distance to Airports

For every site we calculated the distance to the closests airport. There are three type of airports: small, medium and large. Small and medium size airports are typically single runway installations.

f23  <-ggplot(CRN,  aes(Airport_Dist,fill=Airport_Type ))+
  geom_histogram(bins=60,position="stack")+
  ggtitle("Distance to Nearest Airport",subtitle="small medium and large airports")+
  xlab("Distance in km")+ylab("Station Count")

f23

f24  <-ggplot(CRN,  aes(Airport_Dist2,fill=Airport_Type2 ))+
  geom_histogram(bins=60,position="stack")+
  ggtitle("Distance to Nearest Airport",subtitle="medium and large airports")+
  xlab("Distance in km")+ylab("Station Count")

f24

CRN <- CRN %>% arrange( Airport_Dist )
 m5  <-  get_map(location=c(lon=CRN$Airport_Lon[1] ,
                           lat=CRN$Airport_Lat[1]),zoom= 14,maptype="satellite",source="google")            
                
f25  <- ggmap(m5 )+
        geom_point(data=CRN,aes(x=Airport_Lon[1],y=Airport_Lat[1]),color="blue",size=2)+
        ggtitle(CRN$Name[1], subtitle = paste("Airport Type: ", CRN$Airport_Type[1],sep=""))
f25

m6  <- get_map(location=c(lon=CRN$Airport_Lon[2] ,lat=CRN$Airport_Lat[2]),zoom= 15,
                maptype="satellite",source="google")
f26  <- ggmap(m6 ) + geom_point(data=CRN,aes(x=Airport_Lon[2],y=Airport_Lat[2]),color="blue",size=2)+
        ggtitle(CRN$Name[2],subtitle =paste("Airport Type: ", CRN$Airport_Type[2]  ,sep=""))
                                           
f26

Nightlights

f27  <-ggplot(CRN,  aes(Lights))+
  geom_histogram(bins=60)+
  ggtitle("Radiance Calibrated Nightlights") +
  xlab("Lights")+ylab("Station Count")

f27

f28 <- ggplot(CRN, aes(x=GPwV4_10/GPwV4_Area,y=Lights))+geom_point()+
       ggtitle("Lights versus Population Density", subtitle= "circa 2010")+
       xlab("Site Population Density")

f28